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Digital twin in continuous UF/DF step of bioprocess

Continuous technology represents the future tendency of biopharmaceutical processing. Compared to the batch process, the continuous process has benefits of reduced cost, increased productivity, improved product quality and flexibility.

ILC (In-line concentration) and ILDF (In-line diafiltration) are two latest launched facilities, which can fully replace the TFF and enable the continuous UF/DF steps.

It is critical to build up a digital twin of physical continuous UF/DF using a stochastic model integrated with artificial intelligence methods to explore the impact of uncertainties in process parameters on mass throughput and cost of goods (CoGs).

Essential requirement: Good at mathematics, Experienced in programming (in Python or Matlab)

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Yang Yang - Dr. Yang is a Lecturer in Chemical Engineering.  She received her Bachelor and Master degree from Tianjin University and PhD degrees from Leeds University. She worked as a research associate at Imperial College London and University College London. Dr. Yang is a researcher with multidisciplinary academic background in Computer Science (Data Mining and Machine Learning) and Engineering (Chemical Engineering, Biochemical Engineering). Her extensive research interest in Machine Learning applications in cross-field area, especially in bioprocessing and healthcare.